Burning Glass Technologies is a
leading provider of information extraction and predictive analytics software
infrastructure for the Human Capital Management market, applying statistical
natural language processing and information retrieval to predict the degree of
match between a job candidate and the requirements of a position.

·Lead development of probabilistic model for inferring
skills and requirements from stated skills/requirements in job postings and resumés..

·Developed algorithms for efficiently generating
personalized models of document authority and relevance from few examples.

·Developed probabilistic bibliometrics, and the first
published algorithm for reasoning about document contents and links using a
unified probabilistic model. Applications in adaptive web spidering,
clustering, cross-language retrieval and dynamic hypertext generation.

·Core Architect on adaptive document workflow
optimization system for digital pre-press industry. Led design of scheduling
and resource estimation components, which used machine learning and real-time
adaptive scheduling. Managed project involvement of 5 Ph.D.-level members of
the Adaptive Systems Group.

·Initiated and served as Lead Designer on project to
develop document/information management and retrieval system. Designed,
implemented and demonstrated several simpler prototype document
retrieval/clustering systems as testbeds.

·Invented, and led design and implementation of adaptive
memory management system for dynamic memory allocation.